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A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classication

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classication. / Gu, Xiaowei; Angelov, Plamen Parvanov.
The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference. Springer, 2019. p. 257-266.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Gu, X & Angelov, PP 2019, A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classication. in The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference. Springer, pp. 257-266, INNS Conference on Big Data and Deep Learning , 16/04/19. https://doi.org/10.1007/978-3-030-16841-4_27

APA

Gu, X., & Angelov, P. P. (2019). A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classication. In The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference (pp. 257-266). Springer. https://doi.org/10.1007/978-3-030-16841-4_27

Vancouver

Gu X, Angelov PP. A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classication. In The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference. Springer. 2019. p. 257-266 doi: 10.1007/978-3-030-16841-4_27

Author

Gu, Xiaowei ; Angelov, Plamen Parvanov. / A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classication. The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference. Springer, 2019. pp. 257-266

Bibtex

@inproceedings{0d62b832be7f4ca096e3b8e3be8d86ee,
title = "A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classication",
abstract = "This paper proposes a new approach that is based on the recently introduced semi-supervised deep rule-based classifier for remote sensing scene classification. The proposed approach employs a pre-trained deep convoluational neural network as the feature descriptor to extract high-level discriminative semantic features from the sub-regions of the remote sensing images. This approach is able to self-organize a set of prototype-based IF...THEN rules from few labeled training images through an efficient supervised initialization process, and continuously self-updates the rule base with the unlabeled images in an unsupervised, autonomous, transparent and human-interpretable manner. Highly accurate classification on the unlabeled images is performed at the end of the learning process. Numerical examples demonstrate that the proposed approach is a strong alternative to the state-of-the-art ones.",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov}",
year = "2019",
month = apr,
day = "3",
doi = "10.1007/978-3-030-16841-4_27",
language = "English",
isbn = "9783030168407",
pages = "257--266",
booktitle = "The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference",
publisher = "Springer",
note = "INNS Conference on Big Data and Deep Learning ; Conference date: 16-04-2019",
url = "https://innsbddl2019.org/",

}

RIS

TY - GEN

T1 - A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classication

AU - Gu, Xiaowei

AU - Angelov, Plamen Parvanov

PY - 2019/4/3

Y1 - 2019/4/3

N2 - This paper proposes a new approach that is based on the recently introduced semi-supervised deep rule-based classifier for remote sensing scene classification. The proposed approach employs a pre-trained deep convoluational neural network as the feature descriptor to extract high-level discriminative semantic features from the sub-regions of the remote sensing images. This approach is able to self-organize a set of prototype-based IF...THEN rules from few labeled training images through an efficient supervised initialization process, and continuously self-updates the rule base with the unlabeled images in an unsupervised, autonomous, transparent and human-interpretable manner. Highly accurate classification on the unlabeled images is performed at the end of the learning process. Numerical examples demonstrate that the proposed approach is a strong alternative to the state-of-the-art ones.

AB - This paper proposes a new approach that is based on the recently introduced semi-supervised deep rule-based classifier for remote sensing scene classification. The proposed approach employs a pre-trained deep convoluational neural network as the feature descriptor to extract high-level discriminative semantic features from the sub-regions of the remote sensing images. This approach is able to self-organize a set of prototype-based IF...THEN rules from few labeled training images through an efficient supervised initialization process, and continuously self-updates the rule base with the unlabeled images in an unsupervised, autonomous, transparent and human-interpretable manner. Highly accurate classification on the unlabeled images is performed at the end of the learning process. Numerical examples demonstrate that the proposed approach is a strong alternative to the state-of-the-art ones.

U2 - 10.1007/978-3-030-16841-4_27

DO - 10.1007/978-3-030-16841-4_27

M3 - Conference contribution/Paper

SN - 9783030168407

SP - 257

EP - 266

BT - The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference

PB - Springer

T2 - INNS Conference on Big Data and Deep Learning

Y2 - 16 April 2019

ER -